4期

最新文章

• Signal classification for the integrative analysis of multiple sequences of large-scale multiple tests AI翻译
  作者：Xiang, Dongdong; Zhao, Sihai Dave; Cai, T. Tony
  作者单位：East China Normal University; University of Illinois System; University of Illinois Urbana-Champaign; University of Pennsylvania
  摘要：The integrative analysis of multiple data sets is becoming increasingly important in many fields of research. When the same features are studied in several independent experiments, it can often be useful to analyse jointly the multiple sequences of multiple tests that result. It is frequently necessary to classify each feature into one of several categories, depending on the null and non-null configuration of its corresponding test statistics. The paper studies this signal classification probl...
• High dimensional linear discriminant analysis: optimality, adaptive algorithm and missing data AI翻译
  作者：Cai, T. Tony; Zhang, Linjun
  作者单位：University of Pennsylvania
  摘要：The paper develops optimality theory for linear discriminant analysis in the high dimensional setting. A data-driven and tuning-free classification rule, which is based on an adaptive constrained l(1)-minimization approach, is proposed and analysed. Minimax lower bounds are obtained and this classification rule is shown to be simultaneously rate optimal over a collection of parameter spaces. In addition, we consider classification with incomplete data under the missingness completely at random...
• Detecting heteroscedasticity in non-parametric regression using weighted empirical processes (vol 80, pg 951, 2018) AI翻译
  作者：Chown, Justin; Muller, Ursula U.
  作者单位：Ruhr University Bochum; Texas A&M University System; Texas A&M University College Station
• Dynamic shrinkage processes AI翻译
  作者：Kowal, Daniel R.; Matteson, David S.; Ruppert, David
  作者单位：Rice University; Cornell University
  摘要：We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global-local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we model dependence between the local scale parameters. The resulting processes inherit the desirable shrinkage behaviour of popular global-local priors, such as the horseshoe prior, but...
• MALMEM: model averaging in linear measurement error models AI翻译
  作者：Zhang, Xinyu; Ma, Yanyuan; Carroll, Raymond J.
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
  摘要：We develop model averaging estimation in the linear regression model where some covariates are subject to measurement error. The absence of the true covariates in this framework makes the calculation of the standard residual-based loss function impossible. We take advantage of the explicit form of the parameter estimators and construct a weight choice criterion. It is asymptotically equivalent to the unknown model average estimator minimizing the loss function. When the true model is not inclu...
• Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap AI翻译
  作者：Zhao, Qingyuan; Small, Dylan S.; Bhattacharya, Bhaswar B.
  作者单位：University of Pennsylvania
  摘要：To identify the estimand in missing data problems and observational studies, it is common to base the statistical estimation on the 'missingness at random' and 'no unmeasured confounder' assumptions. However, these assumptions are unverifiable by using empirical data and pose serious threats to the validity of the qualitative conclusions of statistical inference. A sensitivity analysis asks how the conclusions may change if the unverifiable assumptions are violated to a certain degree. We cons...